Reinforcement Learning for Caching with Space-Time Popularity Dynamics

TL;DR

This paper proposes a reinforcement learning approach to optimize caching policies in networks by adapting to dynamic space-time content popularity, addressing storage limitations and network interactions.

Contribution

It introduces a versatile RL-based method for near-optimal caching in both single-node and network settings considering space-time popularity dynamics.

Findings

RL policies outperform standard caching methods

Numerical tests validate the approach's effectiveness

Approach adapts to dynamic content popularity

Abstract

With the tremendous growth of data traffic over wired and wireless networks along with the increasing number of rich-media applications, caching is envisioned to play a critical role in next-generation networks. To intelligently prefetch and store contents, a cache node should be able to learn what and when to cache. Considering the geographical and temporal content popularity dynamics, the limited available storage at cache nodes, as well as the interactive in uence of caching decisions in networked caching settings, developing effective caching policies is practically challenging. In response to these challenges, this chapter presents a versatile reinforcement learning based approach for near-optimal caching policy design, in both single-node and network caching settings under dynamic space-time popularities. The herein presented policies are complemented using a set of numerical tests, which showcase the merits of the presented approach relative to several standard caching policies.